Vision-only systems are currently very popular for use in autonomous driving systems and advanced driver-assistance systems. These systems operate by using input images from a camera and no other sensors. One of the tasks these systems needs to perform is the detection and understanding of traffic lights in a traffic environment, by localizing all relevant traffic lights in an image received from an on board camera mounted on the vehicle. This article proposes a traffic light recognition system where adaptive thresholding and deep learning are used for region proposal and traffic light localization, respectively. The LISA open-source dataset is used along with custom augmentation methods in order to increase the number of available data samples. Performance of the developed system is presented in the form of true and false positive rates obtained on the test data. The classification part of the algorithm gives a total of 89.60% true detection rate, while the regression part of the model produced a correct location of the traffic light in 92.67% of cases.

中文翻译：

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自动驾驶系统中的交通信号灯检测

仅视觉系统目前在自动驾驶系统和高级驾驶员辅助系统中非常流行。这些系统通过使用来自摄像机而不是其他传感器的输入图像进行操作。这些系统需要执行的任务之一是，通过将所有相关交通灯定位在从安装在车辆上的车载摄像头接收的图像中，来检测和理解交通环境中的交通灯。本文提出了一种交通信号灯识别系统，其中自适应阈值和深度学习分别用于区域建议和交通信号灯定位。LISA开源数据集与自定义增强方法一起使用，以增加可用数据样本的数量。所开发系统的性能以在测试数据上获得的正确率和错误率表示。该算法的分类部分给出了89.60％的真实检测率，而模型的回归部分则在92.67％的情况下产生了正确的交通信号灯位置。